Resolving patterns of synaptic connectivity in neural circuits currently requires serial section electron microscopy. However, complete circuit reconstruction is prohibitively slow and may not be necessary for many purposes such as comparing neuronal structure and connectivity among multiple animals. Here, we present an alternative strategy, targeted reconstruction of specific neuronal types. We used viral vectors to deliver peroxidase derivatives, which catalyze production of an electron-dense tracer, to genetically identify neurons, and developed a protocol that enhances the electron-density of the labeled cells while retaining the quality of the ultrastructure. The high contrast of the marked neurons enabled two innovations that speed data acquisition: targeted high-resolution reimaging of regions selected from rapidly-acquired lower resolution reconstruction, and an unsupervised segmentation algorithm. This pipeline reduces imaging and reconstruction times by two orders of magnitude, facilitating directed inquiry of circuit motifs.DOI:
http://dx.doi.org/10.7554/eLife.15015.001
BackgroundBolus tracking can individualize time delay for the start of scans in spiral computed tomography (CT).ObjectivesWe compared automatic bolus tracking method with fixed time-delay technique in biphasic contrast enhancement during multidetector CT of abdomen.Patients and MethodsAdult patients referred for spiral CT of the abdomen were randomized into two groups; in group 1, the arterial and portal phases of spiral scans were started 25 s and 55 s after the start of contrast material administration; in group 2, using the automatic bolus tracking software, repetitive monitoring scans were performed within the lumen of the descending aorta as the region of interest with the threshold of starting the diagnostic scans as 60 HU. The contrast enhancement of the aorta, liver, and spleen were compared between the groups.ResultsForty-eight patients (23 males, 25 females, mean age=56.4±13.5 years) were included. The contrast enhancement of the aorta, liver, and spleen at the arterial phase was similar between the two groups (P>0.05). Regarding the portal phase, the aorta and spleen were more enhanced in the bolus-tracking group (P<0.001). The bolus tracking provided more homogeneous contrast enhancement among different patients than the fixed time-delay technique in the liver at portal phase, but not at the arterial phase.ConclusionsThe automatic bolus-tracking method, results in higher contrast enhancement of the aorta and spleen at the portal phase, but has no effect on liver enhancement. However, bolus tracking is associated with reduced variability for liver enhancement among different patients.
Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.
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